Understanding geopandas¶

In [1]:
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
In [2]:
import geopandas

import geopandas as gdp

# uses the "nybb" dataset, a map of New York boroughs, 
# which is part of the GeoPandas installation.

path_to_data = geopandas.datasets.get_path("nybb")

gdf = geopandas.read_file(path_to_data)

gdf

# imported vector files
Out[2]:
BoroCode BoroName Shape_Leng Shape_Area geometry
0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON (((970217.022 145643.332, 970227....
1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON (((1029606.077 156073.814, 102957...
2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON (((1021176.479 151374.797, 102100...
3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON (((981219.056 188655.316, 980940....
4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON (((1012821.806 229228.265, 101278...
In [3]:
type(gdf)
Out[3]:
geopandas.geodataframe.GeoDataFrame
In [4]:
gdf.geometry.values
Out[4]:
<GeometryArray>
[<shapely.geometry.multipolygon.MultiPolygon object at 0x000001561CB82670>,
 <shapely.geometry.multipolygon.MultiPolygon object at 0x000001561CB78520>,
 <shapely.geometry.multipolygon.MultiPolygon object at 0x000001566C119640>,
 <shapely.geometry.multipolygon.MultiPolygon object at 0x000001561CB82A00>,
 <shapely.geometry.multipolygon.MultiPolygon object at 0x000001561C9A8C10>]
Length: 5, dtype: geometry
In [5]:
gdf.crs
Out[5]:
<Projected CRS: EPSG:2263>
Name: NAD83 / New York Long Island (ftUS)
Axis Info [cartesian]:
- X[east]: Easting (US survey foot)
- Y[north]: Northing (US survey foot)
Area of Use:
- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.
- bounds: (-74.26, 40.47, -71.8, 41.3)
Coordinate Operation:
- name: SPCS83 New York Long Island zone (US Survey feet)
- method: Lambert Conic Conformal (2SP)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

image.png

In [6]:
help(gdf.to_crs)

Help on method to_crs in module geopandas.geodataframe:

to_crs(crs=None, epsg=None, inplace=False) method of geopandas.geodataframe.GeoDataFrame instance
    Transform geometries to a new coordinate reference system.
    
    Transform all geometries in an active geometry column to a different coordinate
    reference system.  The ``crs`` attribute on the current GeoSeries must
    be set.  Either ``crs`` or ``epsg`` may be specified for output.
    
    This method will transform all points in all objects. It has no notion
    or projecting entire geometries.  All segments joining points are
    assumed to be lines in the current projection, not geodesics. Objects
    crossing the dateline (or other projection boundary) will have
    undesirable behavior.
    
    Parameters
    ----------
    crs : pyproj.CRS, optional if `epsg` is specified
        The value can be anything accepted by
        :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`,
        such as an authority string (eg "EPSG:4326") or a WKT string.
    epsg : int, optional if `crs` is specified
        EPSG code specifying output projection.
    inplace : bool, optional, default: False
        Whether to return a new GeoDataFrame or do the transformation in
        place.
    
    Returns
    -------
    GeoDataFrame
    
    Examples
    --------
    >>> from shapely.geometry import Point
    >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
    >>> gdf = geopandas.GeoDataFrame(d, crs=4326)
    >>> gdf
        col1                 geometry
    0  name1  POINT (1.00000 2.00000)
    1  name2  POINT (2.00000 1.00000)
    >>> gdf.crs  # doctest: +SKIP
    <Geographic 2D CRS: EPSG:4326>
    Name: WGS 84
    Axis Info [ellipsoidal]:
    - Lat[north]: Geodetic latitude (degree)
    - Lon[east]: Geodetic longitude (degree)
    Area of Use:
    - name: World
    - bounds: (-180.0, -90.0, 180.0, 90.0)
    Datum: World Geodetic System 1984
    - Ellipsoid: WGS 84
    - Prime Meridian: Greenwich
    
    >>> gdf = gdf.to_crs(3857)
    >>> gdf
        col1                       geometry
    0  name1  POINT (111319.491 222684.209)
    1  name2  POINT (222638.982 111325.143)
    >>> gdf.crs  # doctest: +SKIP
    <Projected CRS: EPSG:3857>
    Name: WGS 84 / Pseudo-Mercator
    Axis Info [cartesian]:
    - X[east]: Easting (metre)
    - Y[north]: Northing (metre)
    Area of Use:
    - name: World - 85°S to 85°N
    - bounds: (-180.0, -85.06, 180.0, 85.06)
    Coordinate Operation:
    - name: Popular Visualisation Pseudo-Mercator
    - method: Popular Visualisation Pseudo Mercator
    Datum: World Geodetic System 1984
    - Ellipsoid: WGS 84
    - Prime Meridian: Greenwich
    
    See also
    --------
    GeoDataFrame.set_crs : assign CRS without re-projection
In [7]:
gdf.plot(color='green', edgecolor ='black');
In [8]:
gdf.plot(cmap='jet', edgecolor ='black', column = 'BoroName');
In [9]:
gdf.plot(cmap='jet', 
         edgecolor ='black', 
         column = 'BoroName',
         figsize=(10,100));
In [10]:
ax = gdf.plot(cmap='jet', 
         edgecolor ='red', 
         column = 'BoroName',
         figsize=(10,100));

ax.axis('off');

image.png

image.png

In [11]:
gdf.plot(cmap='jet', edgecolor ='black', column = 'BoroName');
In [12]:
gdf.geometry[0]  # lower left
Out[12]:
In [13]:
gdf.geometry[1]
Out[13]:
In [14]:
gdf.geometry[2]
Out[14]:
In [15]:
gdf.geometry[3]
Out[15]:
In [16]:
gdf.geometry[4]
Out[16]:
In [17]:
# gdf.to_file("my_file.geojson", driver="GeoJSON")
In [18]:
gdf = gdf.set_index("BoroName")
In [19]:
gdf
Out[19]:
BoroCode Shape_Leng Shape_Area geometry
BoroName
Staten Island 5 330470.010332 1.623820e+09 MULTIPOLYGON (((970217.022 145643.332, 970227....
Queens 4 896344.047763 3.045213e+09 MULTIPOLYGON (((1029606.077 156073.814, 102957...
Brooklyn 3 741080.523166 1.937479e+09 MULTIPOLYGON (((1021176.479 151374.797, 102100...
Manhattan 1 359299.096471 6.364715e+08 MULTIPOLYGON (((981219.056 188655.316, 980940....
Bronx 2 464392.991824 1.186925e+09 MULTIPOLYGON (((1012821.806 229228.265, 101278...
In [20]:
gdf["area"] = gdf.area
gdf["area"]
Out[20]:
BoroName
Staten Island    1.623822e+09
Queens           3.045214e+09
Brooklyn         1.937478e+09
Manhattan        6.364712e+08
Bronx            1.186926e+09
Name: area, dtype: float64
In [21]:
gdf['boundary'] = gdf.boundary
gdf['boundary']
Out[21]:
BoroName
Staten Island    MULTILINESTRING ((970217.022 145643.332, 97022...
Queens           MULTILINESTRING ((1029606.077 156073.814, 1029...
Brooklyn         MULTILINESTRING ((1021176.479 151374.797, 1021...
Manhattan        MULTILINESTRING ((981219.056 188655.316, 98094...
Bronx            MULTILINESTRING ((1012821.806 229228.265, 1012...
Name: boundary, dtype: geometry
In [22]:
gdf['centroid'] = gdf.centroid
gdf['centroid']
Out[22]:
BoroName
Staten Island     POINT (941639.450 150931.991)
Queens           POINT (1034578.078 197116.604)
Brooklyn          POINT (998769.115 174169.761)
Manhattan         POINT (993336.965 222451.437)
Bronx            POINT (1021174.790 249937.980)
Name: centroid, dtype: geometry
In [23]:
first_point = gdf['centroid'].iloc[0]
gdf['distance'] = gdf['centroid'].distance(first_point)
gdf['distance']
Out[23]:
BoroName
Staten Island         0.000000
Queens           103781.535276
Brooklyn          61674.893421
Manhattan         88247.742789
Bronx            126996.283623
Name: distance, dtype: float64
In [24]:
gdf['distance'].mean()
Out[24]:
76140.09102166798
In [25]:
gdf.plot("area", legend=True);
In [26]:
gdf.explore("area", legend=False)
Out[26]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [27]:
gdf = gdf.set_geometry("centroid")
gdf.plot("area", legend=True);
In [28]:
ax = gdf["geometry"].plot()
gdf["centroid"].plot(ax=ax, color="black");
In [29]:
gdf = gdf.set_geometry("geometry")
In [30]:
# ex:  countries_gdf.to_file("countries.shp")
# ex:  countries_gdf.to_file("countries.geojson", driver='GeoJSON')
In [31]:
gdf.crs
Out[31]:
<Projected CRS: EPSG:2263>
Name: NAD83 / New York Long Island (ftUS)
Axis Info [cartesian]:
- X[east]: Easting (US survey foot)
- Y[north]: Northing (US survey foot)
Area of Use:
- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.
- bounds: (-74.26, 40.47, -71.8, 41.3)
Coordinate Operation:
- name: SPCS83 New York Long Island zone (US Survey feet)
- method: Lambert Conic Conformal (2SP)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich
In [32]:
gdf.geometry
Out[32]:
BoroName
Staten Island    MULTIPOLYGON (((970217.022 145643.332, 970227....
Queens           MULTIPOLYGON (((1029606.077 156073.814, 102957...
Brooklyn         MULTIPOLYGON (((1021176.479 151374.797, 102100...
Manhattan        MULTIPOLYGON (((981219.056 188655.316, 980940....
Bronx            MULTIPOLYGON (((1012821.806 229228.265, 101278...
Name: geometry, dtype: geometry
In [33]:
type(gdf.geometry)
Out[33]:
geopandas.geoseries.GeoSeries
In [34]:
gdf.geometry.area
Out[34]:
BoroName
Staten Island    1.623822e+09
Queens           3.045214e+09
Brooklyn         1.937478e+09
Manhattan        6.364712e+08
Bronx            1.186926e+09
dtype: float64
In [35]:
gdf.geometry.name
Out[35]:
'geometry'
In [36]:
gdf.crs
Out[36]:
<Projected CRS: EPSG:2263>
Name: NAD83 / New York Long Island (ftUS)
Axis Info [cartesian]:
- X[east]: Easting (US survey foot)
- Y[north]: Northing (US survey foot)
Area of Use:
- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.
- bounds: (-74.26, 40.47, -71.8, 41.3)
Coordinate Operation:
- name: SPCS83 New York Long Island zone (US Survey feet)
- method: Lambert Conic Conformal (2SP)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich
In [37]:
import pandas as pd
import geopandas
import matplotlib.pyplot as plt
In [38]:
df = pd.DataFrame(
    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})
In [39]:
gdf = geopandas.GeoDataFrame(
    df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))
In [40]:
print(gdf.head())

           City    Country  Latitude  Longitude                     geometry
0  Buenos Aires  Argentina    -34.58     -58.66  POINT (-58.66000 -34.58000)
1      Brasilia     Brazil    -15.78     -47.91  POINT (-47.91000 -15.78000)
2      Santiago      Chile    -33.45     -70.66  POINT (-70.66000 -33.45000)
3        Bogota   Colombia      4.60     -74.08    POINT (-74.08000 4.60000)
4       Caracas  Venezuela     10.48     -66.86   POINT (-66.86000 10.48000)
In [41]:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

# We restrict to South America.
ax = world[world.continent == 'South America'].plot(
    color='white', edgecolor='black')

# We can now plot our ``GeoDataFrame``.
gdf.plot(ax=ax, color='red')

plt.show()
In [42]:
url = "http://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_land.geojson"
df2 = geopandas.read_file(url)
df2.plot();
In [43]:
url = "http://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_land.geojson"
dfg = geopandas.read_file(url)
In [44]:
dfg.head(10)
Out[44]:
scalerank featureclass geometry
0 1 Country POLYGON ((-59.57209 -80.04018, -59.86585 -80.5...
1 1 Country POLYGON ((-159.20818 -79.49706, -161.12760 -79...
2 1 Country POLYGON ((-45.15476 -78.04707, -43.92083 -78.4...
3 1 Country POLYGON ((-121.21151 -73.50099, -119.91885 -73...
4 1 Country POLYGON ((-125.55957 -73.48135, -124.03188 -73...
5 1 Country POLYGON ((-98.98155 -71.93333, -97.88474 -72.0...
6 1 Country POLYGON ((-68.45135 -70.95582, -68.33383 -71.4...
7 1 Country POLYGON ((-58.61414 -64.15247, -59.04507 -64.3...
8 1 Country POLYGON ((-67.75000 -53.85000, -66.45000 -54.4...
9 1 Country POLYGON ((-58.55000 -51.10000, -57.75000 -51.5...
In [45]:
dfg.geometry[0]
Out[45]:
In [46]:
dfg.geometry[1]
Out[46]:
In [47]:
dfg.plot();
In [48]:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
In [49]:
world.head()
Out[49]:
pop_est continent name iso_a3 gdp_md_est geometry
0 920938 Oceania Fiji FJI 8374.0 MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 53950935 Africa Tanzania TZA 150600.0 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2 603253 Africa W. Sahara ESH 906.5 POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 35623680 North America Canada CAN 1674000.0 MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 326625791 North America United States of America USA 18560000.0 MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
In [50]:
world.plot();
In [51]:
world = world[(world.pop_est>0) & (world.name!="Antarctica")]

world['gdp_per_cap'] = world.gdp_md_est / world.pop_est

world.plot(column='gdp_per_cap');

C:\Users\tbresee\Anaconda3\lib\site-packages\geopandas\geodataframe.py:1351: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
In [52]:
import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 1)

world.plot(column='pop_est', ax=ax, legend=True);
In [53]:
world.plot(column='gdp_per_cap', cmap='OrRd');
In [54]:
world.boundary.plot();
In [55]:
world.plot(column='gdp_per_cap', cmap='OrRd', scheme='quantiles');
In [56]:
ax = world.plot()
In [57]:
ax = world.plot()

ax.set_axis_off();
In [58]:
cities.plot(marker='*', color='green', markersize=5);


cities = cities.to_crs(world.crs)
In [59]:
base = world.plot(color='white', edgecolor='black')

cities.plot(ax=base, marker='o', color='red', markersize=5);
In [60]:
import matplotlib.pyplot as plt

fig, ax = plt.subplots()




ax.set_aspect('equal')

world.plot(ax=ax, color='white', edgecolor='black')


cities.plot(ax=ax, marker='o', color='red', markersize=5)


plt.show();
In [61]:
from shapely.geometry import Point
In [62]:
%matplotlib inline
In [63]:
list(dir(gdf))
Out[63]:
['City',
 'Country',
 'Latitude',
 'Longitude',
 'T',
 '_AXIS_LEN',
 '_AXIS_ORDERS',
 '_AXIS_REVERSED',
 '_AXIS_TO_AXIS_NUMBER',
 '_HANDLED_TYPES',
 '__abs__',
 '__add__',
 '__and__',
 '__annotations__',
 '__array__',
 '__array_priority__',
 '__array_ufunc__',
 '__array_wrap__',
 '__bool__',
 '__class__',
 '__contains__',
 '__copy__',
 '__deepcopy__',
 '__delattr__',
 '__delitem__',
 '__dict__',
 '__dir__',
 '__divmod__',
 '__doc__',
 '__eq__',
 '__finalize__',
 '__floordiv__',
 '__format__',
 '__ge__',
 '__geo_interface__',
 '__getattr__',
 '__getattribute__',
 '__getitem__',
 '__getstate__',
 '__gt__',
 '__hash__',
 '__iadd__',
 '__iand__',
 '__ifloordiv__',
 '__imod__',
 '__imul__',
 '__init__',
 '__init_subclass__',
 '__invert__',
 '__ior__',
 '__ipow__',
 '__isub__',
 '__iter__',
 '__itruediv__',
 '__ixor__',
 '__le__',
 '__len__',
 '__lt__',
 '__matmul__',
 '__mod__',
 '__module__',
 '__mul__',
 '__ne__',
 '__neg__',
 '__new__',
 '__nonzero__',
 '__or__',
 '__pos__',
 '__pow__',
 '__radd__',
 '__rand__',
 '__rdivmod__',
 '__reduce__',
 '__reduce_ex__',
 '__repr__',
 '__rfloordiv__',
 '__rmatmul__',
 '__rmod__',
 '__rmul__',
 '__ror__',
 '__round__',
 '__rpow__',
 '__rsub__',
 '__rtruediv__',
 '__rxor__',
 '__setattr__',
 '__setitem__',
 '__setstate__',
 '__sizeof__',
 '__str__',
 '__sub__',
 '__subclasshook__',
 '__truediv__',
 '__weakref__',
 '__xor__',
 '_accessors',
 '_accum_func',
 '_add_numeric_operations',
 '_agg_by_level',
 '_agg_examples_doc',
 '_agg_summary_and_see_also_doc',
 '_align_frame',
 '_align_series',
 '_arith_method',
 '_as_manager',
 '_attrs',
 '_box_col_values',
 '_can_fast_transpose',
 '_check_inplace_and_allows_duplicate_labels',
 '_check_inplace_setting',
 '_check_is_chained_assignment_possible',
 '_check_label_or_level_ambiguity',
 '_check_setitem_copy',
 '_clear_item_cache',
 '_clip_with_one_bound',
 '_clip_with_scalar',
 '_cmp_method',
 '_combine_frame',
 '_consolidate',
 '_consolidate_inplace',
 '_construct_axes_dict',
 '_construct_axes_from_arguments',
 '_construct_result',
 '_constructor',
 '_constructor_sliced',
 '_convert',
 '_count_level',
 '_crs',
 '_data',
 '_dir_additions',
 '_dir_deletions',
 '_dispatch_frame_op',
 '_drop_axis',
 '_drop_labels_or_levels',
 '_ensure_valid_index',
 '_find_valid_index',
 '_flags',
 '_from_arrays',
 '_from_mgr',
 '_geometry_column_name',
 '_get_agg_axis',
 '_get_axis',
 '_get_axis_name',
 '_get_axis_number',
 '_get_axis_resolvers',
 '_get_block_manager_axis',
 '_get_bool_data',
 '_get_cleaned_column_resolvers',
 '_get_column_array',
 '_get_geometry',
 '_get_index_resolvers',
 '_get_item_cache',
 '_get_label_or_level_values',
 '_get_numeric_data',
 '_get_value',
 '_getitem_bool_array',
 '_getitem_multilevel',
 '_gotitem',
 '_hidden_attrs',
 '_indexed_same',
 '_info_axis',
 '_info_axis_name',
 '_info_axis_number',
 '_info_repr',
 '_init_mgr',
 '_inplace_method',
 '_internal_names',
 '_internal_names_set',
 '_is_copy',
 '_is_homogeneous_type',
 '_is_label_or_level_reference',
 '_is_label_reference',
 '_is_level_reference',
 '_is_mixed_type',
 '_is_view',
 '_iset_item',
 '_iset_item_mgr',
 '_iset_not_inplace',
 '_item_cache',
 '_iter_column_arrays',
 '_ixs',
 '_join_compat',
 '_logical_func',
 '_logical_method',
 '_maybe_cache_changed',
 '_maybe_update_cacher',
 '_metadata',
 '_mgr',
 '_min_count_stat_function',
 '_needs_reindex_multi',
 '_protect_consolidate',
 '_reduce',
 '_reindex_axes',
 '_reindex_columns',
 '_reindex_index',
 '_reindex_multi',
 '_reindex_with_indexers',
 '_replace_columnwise',
 '_repr_data_resource_',
 '_repr_fits_horizontal_',
 '_repr_fits_vertical_',
 '_repr_html_',
 '_repr_latex_',
 '_reset_cache',
 '_reset_cacher',
 '_sanitize_column',
 '_series',
 '_set_axis',
 '_set_axis_name',
 '_set_axis_nocheck',
 '_set_geometry',
 '_set_is_copy',
 '_set_item',
 '_set_item_frame_value',
 '_set_item_mgr',
 '_set_value',
 '_setitem_array',
 '_setitem_frame',
 '_setitem_slice',
 '_slice',
 '_stat_axis',
 '_stat_axis_name',
 '_stat_axis_number',
 '_stat_function',
 '_stat_function_ddof',
 '_take_with_is_copy',
 '_to_dict_of_blocks',
 '_to_geo',
 '_typ',
 '_update_inplace',
 '_validate_dtype',
 '_values',
 '_where',
 'abs',
 'add',
 'add_prefix',
 'add_suffix',
 'affine_transform',
 'agg',
 'aggregate',
 'align',
 'all',
 'any',
 'append',
 'apply',
 'applymap',
 'area',
 'asfreq',
 'asof',
 'assign',
 'astype',
 'at',
 'at_time',
 'attrs',
 'axes',
 'backfill',
 'between_time',
 'bfill',
 'bool',
 'boundary',
 'bounds',
 'boxplot',
 'buffer',
 'cascaded_union',
 'centroid',
 'clip',
 'columns',
 'combine',
 'combine_first',
 'compare',
 'contains',
 'convert_dtypes',
 'convex_hull',
 'copy',
 'corr',
 'corrwith',
 'count',
 'cov',
 'covered_by',
 'covers',
 'crosses',
 'crs',
 'cummax',
 'cummin',
 'cumprod',
 'cumsum',
 'cx',
 'describe',
 'diff',
 'difference',
 'disjoint',
 'dissolve',
 'distance',
 'div',
 'divide',
 'dot',
 'drop',
 'drop_duplicates',
 'droplevel',
 'dropna',
 'dtypes',
 'duplicated',
 'empty',
 'envelope',
 'eq',
 'equals',
 'estimate_utm_crs',
 'eval',
 'ewm',
 'expanding',
 'explode',
 'explore',
 'exterior',
 'ffill',
 'fillna',
 'filter',
 'first',
 'first_valid_index',
 'flags',
 'floordiv',
 'from_dict',
 'from_features',
 'from_file',
 'from_postgis',
 'from_records',
 'ge',
 'geom_almost_equals',
 'geom_equals',
 'geom_equals_exact',
 'geom_type',
 'geometry',
 'get',
 'groupby',
 'gt',
 'has_sindex',
 'has_z',
 'head',
 'hist',
 'iat',
 'idxmax',
 'idxmin',
 'iloc',
 'index',
 'infer_objects',
 'info',
 'insert',
 'interiors',
 'interpolate',
 'intersection',
 'intersects',
 'is_empty',
 'is_ring',
 'is_simple',
 'is_valid',
 'isin',
 'isna',
 'isnull',
 'items',
 'iterfeatures',
 'iteritems',
 'iterrows',
 'itertuples',
 'join',
 'keys',
 'kurt',
 'kurtosis',
 'last',
 'last_valid_index',
 'le',
 'length',
 'loc',
 'lookup',
 'lt',
 'mad',
 'mask',
 'max',
 'mean',
 'median',
 'melt',
 'memory_usage',
 'merge',
 'min',
 'mod',
 'mode',
 'mul',
 'multiply',
 'ndim',
 'ne',
 'nlargest',
 'notna',
 'notnull',
 'nsmallest',
 'nunique',
 'overlaps',
 'overlay',
 'pad',
 'pct_change',
 'pipe',
 'pivot',
 'pivot_table',
 'plot',
 'pop',
 'pow',
 'prod',
 'product',
 'project',
 'quantile',
 'query',
 'radd',
 'rank',
 'rdiv',
 'reindex',
 'reindex_like',
 'relate',
 'rename',
 'rename_axis',
 'rename_geometry',
 'reorder_levels',
 'replace',
 'representative_point',
 'resample',
 'reset_index',
 'rfloordiv',
 'rmod',
 'rmul',
 'rolling',
 'rotate',
 'round',
 'rpow',
 'rsub',
 'rtruediv',
 'sample',
 'scale',
 'select_dtypes',
 'sem',
 'set_axis',
 'set_crs',
 'set_flags',
 'set_geometry',
 'set_index',
 'shape',
 'shift',
 'simplify',
 'sindex',
 'size',
 'sjoin',
 'sjoin_nearest',
 'skew',
 'slice_shift',
 'sort_index',
 'sort_values',
 'squeeze',
 'stack',
 'std',
 'style',
 'sub',
 'subtract',
 'sum',
 'swapaxes',
 'swaplevel',
 'symmetric_difference',
 'tail',
 'take',
 'to_clipboard',
 'to_crs',
 'to_csv',
 'to_dict',
 'to_excel',
 'to_feather',
 'to_file',
 'to_gbq',
 'to_hdf',
 'to_html',
 'to_json',
 'to_latex',
 'to_markdown',
 'to_numpy',
 'to_parquet',
 'to_period',
 'to_pickle',
 'to_postgis',
 'to_records',
 'to_sql',
 'to_stata',
 'to_string',
 'to_timestamp',
 'to_wkb',
 'to_wkt',
 'to_xarray',
 'to_xml',
 'total_bounds',
 'touches',
 'transform',
 'translate',
 'transpose',
 'truediv',
 'truncate',
 'type',
 'tz_convert',
 'tz_localize',
 'unary_union',
 'union',
 'unstack',
 'update',
 'value_counts',
 'values',
 'var',
 'where',
 'within',
 'xs']
In [64]:
help(geopandas)

Help on package geopandas:

NAME
    geopandas

PACKAGE CONTENTS
    _compat
    _config
    _decorator
    _vectorized
    _version
    array
    base
    conftest
    datasets (package)
    explore
    geodataframe
    geoseries
    io (package)
    plotting
    sindex
    testing
    tests (package)
    tools (package)

DATA
    options = Options(
      display_precision: None [default: Non...USE_PYGEO...

VERSION
    0.10.2

FILE
    c:\users\tbresee\anaconda3\lib\site-packages\geopandas\__init__.py

image.png

In [65]:
# states = gdp.read_file('some.shp')
In [66]:
z = gdp.read_file(r'D:\mexico\cities.shp')  # only because it needed all the other files
In [67]:
z
Out[67]:
NAME CAPITAL STATE_NAME POPULATION geometry
0 Monterrey Y Nuevo Leon 2015000 POINT (-100.31709 25.67735)
1 Mazatlan N Sinaloa 199830 POINT (-106.41607 23.20383)
2 Guadalajara Y Jalisco 2325000 POINT (-103.34380 20.67359)
3 Tampico N Tamaulipas 435000 POINT (-97.84263 22.24323)
4 Mexico City C Distrito Federal 14100000 POINT (-99.12757 19.42705)
5 Puebla de Zaragoza Y Puebla 1055000 POINT (-98.19295 19.04863)
6 Veracruz N Veracruz-Llave 385000 POINT (-96.08524 19.00683)
7 Oaxaca Y Oaxaca 154223 POINT (-96.95135 16.90743)
8 Merida Y Yucatan 400142 POINT (-89.55286 20.82187)
9 Mexicali Y Baja California Norte 365000 POINT (-115.29424 32.62020)
10 Aguascalientes Y Aguascalientes 293152 POINT (-102.18634 21.85335)
11 Campeche Y Campeche 128434 POINT (-90.54466 19.80399)
12 La Paz Y Baja California Sur 91453 POINT (-110.25386 24.18983)
13 Tuxtla Gutierrez Y Chiapas 131096 POINT (-92.99516 16.63030)
14 Chihuahua Y Chihuahua 385603 POINT (-105.97516 28.56030)
15 Saltillo Y Coahuila De Zaragoza 284937 POINT (-100.99583 25.44186)
16 Colima Y Colima 86044 POINT (-103.68410 19.21037)
17 Durango Y Durango 257915 POINT (-104.40000 24.02000)
18 Guanajuato Y Guanajuato 48981 POINT (-101.15000 21.01000)
19 Chilpancingo Y Guerrero 67498 POINT (-99.38337 17.35706)
20 Pachuca Y Hidalgo 110351 POINT (-98.70045 20.09706)
21 Morelia Y Michoacan de Ocampo 297544 POINT (-101.07320 19.66335)
22 Toluca Y Mexico 199778 POINT (-99.53128 19.32050)
23 Cuernavaca Y Morelos 192770 POINT (-99.18118 18.95008)
24 Tepic Y Nayarit 145741 POINT (-104.78242 21.53045)
25 Queretaro Y Queretaro de Arteaga 215976 POINT (-100.24117 20.50074)
26 Chetumal Y Quintana Roo 56709 POINT (-88.26436 18.55124)
27 San Luis Potosi Y San Luis Potosi 470000 POINT (-100.96924 22.13672)
28 Culiacan Y Sinaloa 304826 POINT (-107.45556 24.71334)
29 Hermosillo Y Sonora 297175 POINT (-110.83422 29.02044)
30 Villahermosa Y Tabasco 158216 POINT (-92.86255 17.96756)
31 Tlaxcala Y Tlaxcala 35384 POINT (-98.19723 19.27763)
32 Jalapa Y Veracruz-Llave 204594 POINT (-96.92548 19.46773)
33 Zacatecas Y Zacatecas 80088 POINT (-102.72294 22.75922)
34 Ciudad Victoria Y Tamaulipas 140161 POINT (-99.14811 23.73515)
35 Acapulco N Guerrero 301902 POINT (-99.93150 16.97439)
In [68]:
z.plot();

image.png